Classification of weld defects using machine vision using convolutional neural network

Abstract

Welding is an important aspect in commercial use of almost every industry. Because weld flaws can cause irregularities or inconsistencies during welding process, welding quality control is a critical step in ensuring the product’s quality and overall longevity. This study focuses on recognizing contamination defects, lack of fusion defects, or if the weld belongs to the good weld category among the defects that occur during the welding process. This category categorization is carried out for the Convolutional Neural Network (CNN) algorithm and the accuracy metric is obtained to evaluate the efficiency of the algorithm for the 3 – class dataset. According to this research, the pure CNN approach gave an accuracy result of 96.1%.